code: def train_one_epoch(
      epoch,
      model,
      loader,
      optimizer,
      loss_fn,
      args,
      lr_scheduler=None,
      saver=None,
      output_dir=None,
      amp_autocast=suppress,
      loss_scaler=None,
      model_ema=None,
      mixup_fn=None,
      wd_scheduler=None,
  ):
      if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
          if args.prefetcher and loader.mixup_enabled:
              loader.mixup_enabled = False
          elif mixup_fn is not None:
              mixup_fn.mixup_enabled = False
  
      second_order = (
          hasattr(optimizer, "is_second_order")
          and optimizer.is_second_order
      )
      batch_time_m = AverageMeter()
      data_time_m = AverageMeter()
      losses_m = AverageMeter()
  
      model.train()
  
      end = time.time()
      last_idx = len(loader) - 1
      num_updates = epoch * len(loader)
      for batch_idx, (input, target) in enumerate(loader):
          last_batch = batch_idx == last_idx
          data_time_m.update(time.time() - end)
          if not args.prefetcher:
              input, target = input.cuda(), target.cuda()
              if mixup_fn is not None:
                  input, target = mixup_fn(input, target)
          if args.channels_last:
              input = input.contiguous(
                  memory_format=torch.channels_last
              )
  
          with amp_autocast():
              output = model(input)
              loss = loss_fn(input, output, target)
  
          if not args.distributed:
              losses_m.update(loss.item(), input.size(0))
  
          optimizer.zero_grad()
          if loss_scaler is not None:
              loss_scaler(
                  loss,
                  optimizer,
                  clip_grad=args.clip_grad,
                  clip_mode=args.clip_mode,
                  parameters=model_parameters(
                      model, exclude_head="agc" in args.clip_mode
                  ),
                  create_graph=second_order,
              )
          else:
              loss.backward(create_graph=second_order)
              if args.clip_grad is not None:
                  dispatch_clip_grad(
                      model_parameters(
                          model, exclude_head="agc" in args.clip_mode
                      ),
                      value=args.clip_grad,
                      mode=args.clip_mode,
                  )
              optimizer.step()
  
          if model_ema is not None:
              model_ema.update(model)
  
          torch.cuda.synchronize()
          num_updates += 1
          batch_time_m.update(time.time() - end)
          if last_batch or batch_idx % args.log_interval == 0:
              lrl = [
                  param_group["lr"]
                  for param_group in optimizer.param_groups
              ]
              lr = sum(lrl) / len(lrl)
              wd0 = list(optimizer.param_groups)[0]["weight_decay"]
              wd1 = list(optimizer.param_groups)[1]["weight_decay"]
  
              if args.distributed:
                  reduced_loss = reduce_tensor(
                      loss.data, args.world_size
                  )
                  losses_m.update(reduced_loss.item(), input.size(0))
  
              if args.local_rank == 0:
                  _logger.info(
                      "Train: {} [{:>4d}/{} ({:>3.0f}%)]  "
                      "Loss: {loss.val:#.4g} ({loss.avg:#.3g})  "
                      "Time: {batch_time.val:.3f}s, {rate:>7.2f}/s  "
                      "({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  "
                      "LR: {lr:.3e}, WD0: {wd0:.6e}, WD1: {wd1:.6e}    "
                      "Data: {data_time.val:.3f} ({data_time.avg:.3f})".format(
                          epoch,
                          batch_idx,
                          len(loader),
                          100.0 * batch_idx / last_idx,
                          loss=losses_m,
                          batch_time=batch_time_m,
                          rate=input.size(0)
                          * args.world_size
                          / batch_time_m.val,
                          rate_avg=input.size(0)
                          * args.world_size
                          / batch_time_m.avg,
                          lr=lr,
                          wd0=wd0,
                          wd1=wd1,
                          data_time=data_time_m,
                      )
                  )
  
                  if args.save_images and output_dir:
                      torchvision.utils.save_image(
                          input,
                          os.path.join(
                              output_dir,
                              "train-batch-%d.jpg" % batch_idx,
                          ),
                          padding=0,
                          normalize=True,
                      )
  
          if (
              saver is not None
              and args.recovery_interval
              and (
                  last_batch
                  or (batch_idx + 1) % args.recovery_interval == 0
              )
          ):
              saver.save_recovery(epoch, batch_idx=batch_idx)
  
          if lr_scheduler is not None:
              lr_scheduler.step_update(
                  num_updates=num_updates, metric=losses_m.avg
              )
          if wd_scheduler is not None:
              wd_scheduler.update_weight_decay(optimizer)
  
          end = time.time()
          # end for
  
      if hasattr(optimizer, "sync_lookahead"):
          optimizer.sync_lookahead()
  
      return OrderedDict([("loss", losses_m.avg)])
  
imports: import argparse
  import time
  import yaml
  import os
  import glob
  import math
  import logging
  from collections import OrderedDict
  from contextlib import suppress
  from datetime import datetime
  import torch
  import torch.nn
  import torchvision.utils
  from torch.nn.parallel import DistributedDataParallel
  from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
  from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint, model_parameters
  from timm.layers import convert_splitbn_model
  from timm.utils import *
  from timm.loss import *
  from timm.optim import create_optimizer_v2, optimizer_kwargs
  from timm.scheduler import create_scheduler
  from timm.utils import ApexScaler, NativeScaler
  import models
  from misc.distillation_loss import DistillationLoss
  from misc.cosine_annealing import CosineWDSchedule
label_desc: Use image node_id 3 for calling a global function with example usage: train_one_epoch(epoch, model, loader, optimizer, loss_fn, args, lr_scheduler, saver, output_dir, amp_autocast, loss_scaler, model_ema, mixup_fn, wd_scheduler) and returns: OrderedDict